# VCA
if run_vca:
from turbustat.statistics import VCA_Distance
cube = fits.open(
osjoin(data_path, "Design4_flatrho_0021_00_radmc.fits"))[0]
cube_fid = fits.open(
osjoin(data_path, "Fiducial0_flatrho_0021_00_radmc.fits"))[0]
vca = VCA_Distance(cube_fid,
cube,
low_cut=0.025 / u.pix,
high_cut=0.1 / u.pix)
vca.distance_metric(verbose=True,
save_name=osjoin(fig_path, "vca_distmet.png"))
print(vca.distance)
vca = VCA_Distance(cube_fid,
cube,
low_cut=0.025 / u.pix,
high_cut=0.1 / u.pix,
channel_width=400 * u.m / u.s)
vca.distance_metric(verbose=True,
save_name=osjoin(fig_path,
"vca_distmet_thickchan.png"))
print(vca.distance)
# VCS
if run_vcs:
def stats_wrapper(dataset1, dataset2, fiducial_models=None,
statistics=None, multicore=False, vca_break=None,
vcs_break=None, vcs_regrid=[None, None],
dendro_params=None,
periodic_bounds=[True, True],
noise_value=[-np.inf, -np.inf],
dendro_saves=[None, None],
scf_saves=[None, None],
inertial_range=[[None] * 2, [None] * 2],
spatial_range=[[None] * 2, [None] * 2]):
'''
Function to run all of the statistics on two datasets.
Each statistic is run with set inputs. This function needs to be altered
to change the inputs.
Parameters
----------
dataset1 : dict
Contains the cube and all of its property arrays.
dataset2 : dict
See dataset1
fiducial_models : dict, optional
Models for dataset1. Avoids recomputing when comparing
many sets to dataset1.
statistics : list, optional
List of all of the statistics to use. If None, all are run.
multicore : bool, optional
If the wrapper is being used in parallel, this disables
returning model values for dataset1.
vcs_break : float, optional
Pass an initial guess for the location of the VCS break.
vcs_regrid : list of bools, optional
The simulated cubes lack information on the smallest spectral scales.
When enabled, the cube is downsampled by a factor of 5 spectrally
before running VCS.
dendro_params : dict or list, optional
Provides parameters to use when computing the initial dendrogram.
If different parameters are required for each dataset, the
the input should be a list containing the two dictionaries.
periodic_bounds : list of bools
Set whether the boundaries should be handled as 'continuous' (True) or
not ('cut' or 'fill'; False).
cleanup : bool, optional
Delete distance classes after running.
'''
if statistics is None: # Run them all
statistics = statistics_list
distances = {}
# Calculate the fiducial case and return it for later use
if fiducial_models is None:
fiducial_models = {}
for statistic in statistics:
if "PDF" in statistic:
statistic = "PDF"
elif statistic == "Skewness" or statistic == "Kurtosis":
statistic = "stat_moments"
elif "Dendrogram" in statistic:
statistic = "Dendrogram"
elif "DeltaVariance_Centroid" in statistic:
statistic = "DeltaVariance_Centroid"
elif "DeltaVariance" in statistic and "Centroid" not in statistic:
statistic = "DeltaVariance"
fiducial_models[statistic] = None
if any("Wavelet" in s for s in statistics):
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models["Wavelet"],
xlow=spatial_range[0],
xhigh=spatial_range[1])
wavelet_distance.distance_metric()
distances["Wavelet"] = wavelet_distance.distance
if not multicore:
fiducial_models["Wavelet"] = copy(wavelet_distance.wt1)
del wavelet_distance
if any("MVC" in s for s in statistics):
mvc_distance = \
MVC_Distance(dataset1, dataset2,
fiducial_model=fiducial_models["MVC"],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
mvc_distance.distance_metric()
distances["MVC"] = mvc_distance.distance
if not multicore:
fiducial_models["MVC"] = copy(mvc_distance.mvc1)
del mvc_distance
if any("PSpec" in s for s in statistics):
pspec_distance = \
PSpec_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['PSpec'],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
pspec_distance.distance_metric()
distances["PSpec"] = pspec_distance.distance
if not multicore:
fiducial_models["PSpec"] = copy(pspec_distance.pspec1)
del pspec_distance
if any("Bispectrum" in s for s in statistics):
bispec_distance = \
BiSpectrum_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Bispectrum'])
bispec_distance.distance_metric()
distances["Bispectrum"] = bispec_distance.distance
if not multicore:
fiducial_models["Bispectrum"] = copy(bispec_distance.bispec1)
del bispec_distance
if any("DeltaVariance_Slope" in s for s in statistics) or \
any("DeltaVariance_Curve" in s for s in statistics):
# Check for how boundaries should be handled.
boundary1 = 'wrap' if periodic_bounds[0] else 'fill'
boundary2 = 'wrap' if periodic_bounds[1] else 'fill'
delvar_distance = \
DeltaVariance_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0]**-2,
weights2=dataset2["moment0_error"][0]**-2,
fiducial_model=fiducial_models["DeltaVariance"],
xlow=spatial_range[0],
xhigh=spatial_range[1],
boundary=[boundary1, boundary2])
delvar_distance.distance_metric()
distances["DeltaVariance_Curve"] = delvar_distance.curve_distance
distances["DeltaVariance_Slope"] = delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance"] = copy(delvar_distance.delvar1)
del delvar_distance
if any("DeltaVariance_Centroid_Slope" in s for s in statistics) or \
any("DeltaVariance_Centroid_Curve" in s for s in statistics):
# Check for how boundaries should be handled.
boundary1 = 'wrap' if periodic_bounds[0] else 'fill'
boundary2 = 'wrap' if periodic_bounds[1] else 'fill'
delvar_distance = \
DeltaVariance_Distance(dataset1["centroid"],
dataset2["centroid"],
weights1=dataset1["centroid_error"][0]**-2,
weights2=dataset2["centroid_error"][0]**-2,
fiducial_model=fiducial_models["DeltaVariance_Centroid"],
xlow=spatial_range[0],
xhigh=spatial_range[1],
boundary=[boundary1, boundary2])
delvar_distance.distance_metric()
distances["DeltaVariance_Centroid_Curve"] = \
delvar_distance.curve_distance
distances["DeltaVariance_Centroid_Slope"] = \
delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance_Centroid"] = \
copy(delvar_distance.delvar1)
del delvar_distance
if any("Genus" in s for s in statistics):
genus_distance = \
GenusDistance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Genus'])
genus_distance.distance_metric()
distances["Genus"] = genus_distance.distance
if not multicore:
fiducial_models["Genus"] = copy(genus_distance.genus1)
del genus_distance
if any("VCS" in s for s in statistics):
# Regrid the cube to lower spectral resolution
if any(vcs_regrid):
from spectral_cube import SpectralCube
import astropy.io.fits as fits
from analysis_funcs import spectral_regrid_cube
if vcs_regrid[0] is not None:
cube1_hdu = fits.PrimaryHDU(dataset1["cube"][0],
header=dataset1["cube"][1])
cube1 = SpectralCube.read(cube1_hdu)
cube1 = spectral_regrid_cube(cube1, int(vcs_regrid[0]))
else:
cube1 = dataset1["cube"]
if vcs_regrid[1] is not None:
cube2_hdu = fits.PrimaryHDU(dataset2["cube"][0],
header=dataset2["cube"][1])
cube2 = SpectralCube.read(cube2_hdu)
cube2 = spectral_regrid_cube(cube2, int(vcs_regrid[1]))
else:
cube2 = dataset2["cube"]
else:
cube1 = dataset1["cube"]
cube2 = dataset2["cube"]
vcs_distance = VCS_Distance(cube1,
cube2,
breaks=vcs_break,
fiducial_model=fiducial_models['VCS'])
vcs_distance.distance_metric()
distances["VCS"] = vcs_distance.distance
distances["VCS_Small_Scale"] = vcs_distance.small_scale_distance
distances["VCS_Large_Scale"] = vcs_distance.large_scale_distance
distances["VCS_Break"] = vcs_distance.break_distance
if not multicore:
fiducial_models["VCS"] = copy(vcs_distance.vcs1)
del vcs_distance
if any("VCA" in s for s in statistics):
vca_distance = VCA_Distance(dataset1["cube"],
dataset2["cube"],
breaks=vca_break,
fiducial_model=fiducial_models['VCA'],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
vca_distance.distance_metric()
distances["VCA"] = vca_distance.distance
if not multicore:
fiducial_models["VCA"] = copy(vca_distance.vca1)
del vca_distance
if any("Tsallis" in s for s in statistics):
tsallis_distance = \
Tsallis_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Tsallis'])
tsallis_distance.distance_metric()
distances["Tsallis"] = tsallis_distance.distance
if not multicore:
fiducial_models["Tsallis"] = copy(tsallis_distance.tsallis1)
del tsallis_distance
if any("Skewness" in s for s in statistics) or\
any("Kurtosis" in s for s in statistics):
moment_distance = \
StatMoments_Distance(dataset1["moment0"],
dataset2["moment0"], radius=5,
weights1=dataset1["moment0_error"][0]**-2,
weights2=dataset2["moment0_error"][0]**-2,
fiducial_model=fiducial_models['stat_moments'])
moment_distance.distance_metric()
distances["Skewness"] = moment_distance.skewness_distance
distances["Kurtosis"] = moment_distance.kurtosis_distance
if not multicore:
fiducial_models["stat_moments"] = \
copy(moment_distance.moments1)
del moment_distance
if any("PCA" in s for s in statistics):
pca_distance = \
PCA_Distance(dataset1["cube"],
dataset2["cube"],
fiducial_model=fiducial_models['PCA'])
pca_distance.distance_metric()
distances["PCA"] = pca_distance.distance
if not multicore:
fiducial_models["PCA"] = pca_distance.pca1
del pca_distance
if any("SCF" in s for s in statistics):
# Switch the inputs such that the save file is the "fiducial"
# or first cube input below
# if scf_saves[0] is not None:
# fid_model = SCF.load_results(scf_saves[0])
# cube1 = dataset1["cube"]
# cube2 = dataset2["cube"]
# boundary1 = "continuous" if periodic_bounds[0] else 'cut'
# boundary2 = "continuous" if periodic_bounds[1] else 'cut'
# if scf_saves[1] is not None:
# fid_model = SCF.load_results(scf_saves[1])
# cube2 = dataset1["cube"]
# cube1 = dataset2["cube"]
# boundary1 = "continuous" if periodic_bounds[1] else 'cut'
# boundary2 = "continuous" if periodic_bounds[0] else 'cut'
boundary1 = "continuous" if periodic_bounds[0] else 'cut'
boundary2 = "continuous" if periodic_bounds[1] else 'cut'
scf_distance = \
SCF_Distance(cube1, cube2,
boundary=[boundary1, boundary2],
fiducial_model=fiducial_models["SCF"])
# fiducial_model=fid_model)
scf_distance.distance_metric()
distances["SCF"] = scf_distance.distance
if not multicore:
fiducial_models["SCF"] = copy(scf_distance.scf1)
del scf_distance
if any("Cramer" in s for s in statistics):
cramer_distance = \
Cramer_Distance(dataset1["cube"],
dataset2["cube"],
noise_value1=noise_value[0],
noise_value2=noise_value[1]).distance_metric()
distances["Cramer"] = cramer_distance.distance
del cramer_distance
if any("Dendrogram_Hist" in s for s in statistics) or \
any("Dendrogram_Num" in s for s in statistics):
if dendro_saves[0] is None:
input1 = dataset1["cube"]
elif isinstance(dendro_saves[0], str):
input1 = dendro_saves[0]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
if dendro_saves[1] is None:
input2 = dataset2["cube"]
elif isinstance(dendro_saves[1], str):
input2 = dendro_saves[1]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
dendro_distance = \
DendroDistance(input1, input2,
dendro_params=dendro_params,
fiducial_model=fiducial_models['Dendrogram'],
periodic_bounds=periodic_bounds,
min_features=40)
dendro_distance.distance_metric()
distances["Dendrogram_Hist"] = dendro_distance.histogram_distance
distances["Dendrogram_Num"] = dendro_distance.num_distance
if not multicore:
fiducial_models["Dendrogram"] = copy(dendro_distance.dendro1)
del dendro_distance
if any("PDF_Hellinger" in s for s in statistics) or \
any("PDF_KS" in s for s in statistics) or \
any("PDF_Lognormal" in s for s in statistics): # or \
# any("PDF_AD" in s for s in statistics):
pdf_distance = \
PDF_Distance(dataset1["moment0"],
dataset2["moment0"],
min_val1=2 * noise_value[0],
min_val2=2 * noise_value[1])
pdf_distance.distance_metric()
distances["PDF_Hellinger"] = pdf_distance.hellinger_distance
distances["PDF_KS"] = pdf_distance.ks_distance
distances["PDF_Lognormal"] = pdf_distance.lognormal_distance
# distances["PDF_AD"] = pdf_distance.ad_distance
if not multicore:
fiducial_models["PDF"] = copy(pdf_distance.PDF1)
del pdf_distance
if multicore:
return distances
else:
return distances, fiducial_models
# tsallis = Tsallis(moment0).run(verbose=True, sigma_clip=3,
# save_name=osjoin(fig_path, 'design4_tsallis_sigclip.png'))
# tsallis.plot_parameters(save_name=osjoin(fig_path, 'design4_tsallis_params_sigclip.png'))
# VCA
if run_vca:
from turbustat.statistics import VCA_Distance
cube = fits.open(osjoin(data_path, "Design4_flatrho_0021_00_radmc.fits"))[0]
cube_fid = fits.open(osjoin(data_path, "Fiducial0_flatrho_0021_00_radmc.fits"))[0]
vca = VCA_Distance(cube_fid, cube, low_cut=0.025 / u.pix,
high_cut=0.1 / u.pix)
vca.distance_metric(verbose=True,
save_name=osjoin(fig_path, "vca_distmet.png"))
print(vca.distance)
vca = VCA_Distance(cube_fid, cube, low_cut=0.025 / u.pix,
high_cut=0.1 / u.pix, channel_width=400 * u.m / u.s)
vca.distance_metric(verbose=True,
save_name=osjoin(fig_path, "vca_distmet_thickchan.png"))
print(vca.distance)
# VCS
if run_vcs:
from turbustat.statistics import VCS_Distance
def stats_wrapper(dataset1,
dataset2,
fiducial_models=None,
statistics=None,
multicore=False,
vca_break=None,
vcs_break=None,
vcs_regrid=[None, None],
dendro_params=None,
periodic_bounds=[True, True],
noise_value=[-np.inf, -np.inf],
dendro_saves=[None, None],
scf_saves=[None, None],
inertial_range=[[None] * 2, [None] * 2],
spatial_range=[[None] * 2, [None] * 2]):
'''
Function to run all of the statistics on two datasets.
Each statistic is run with set inputs. This function needs to be altered
to change the inputs.
Parameters
----------
dataset1 : dict
Contains the cube and all of its property arrays.
dataset2 : dict
See dataset1
fiducial_models : dict, optional
Models for dataset1. Avoids recomputing when comparing
many sets to dataset1.
statistics : list, optional
List of all of the statistics to use. If None, all are run.
multicore : bool, optional
If the wrapper is being used in parallel, this disables
returning model values for dataset1.
vcs_break : float, optional
Pass an initial guess for the location of the VCS break.
vcs_regrid : list of bools, optional
The simulated cubes lack information on the smallest spectral scales.
When enabled, the cube is downsampled by a factor of 5 spectrally
before running VCS.
dendro_params : dict or list, optional
Provides parameters to use when computing the initial dendrogram.
If different parameters are required for each dataset, the
the input should be a list containing the two dictionaries.
periodic_bounds : list of bools
Set whether the boundaries should be handled as 'continuous' (True) or
not ('cut' or 'fill'; False).
cleanup : bool, optional
Delete distance classes after running.
'''
if statistics is None: # Run them all
statistics = statistics_list
distances = {}
# Calculate the fiducial case and return it for later use
if fiducial_models is None:
fiducial_models = {}
for statistic in statistics:
if "PDF" in statistic:
statistic = "PDF"
elif statistic == "Skewness" or statistic == "Kurtosis":
statistic = "stat_moments"
elif "Dendrogram" in statistic:
statistic = "Dendrogram"
elif "DeltaVariance_Centroid" in statistic:
statistic = "DeltaVariance_Centroid"
elif "DeltaVariance" in statistic and "Centroid" not in statistic:
statistic = "DeltaVariance"
fiducial_models[statistic] = None
if any("Wavelet" in s for s in statistics):
wavelet_distance = \
Wavelet_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models["Wavelet"],
xlow=spatial_range[0],
xhigh=spatial_range[1])
wavelet_distance.distance_metric()
distances["Wavelet"] = wavelet_distance.distance
if not multicore:
fiducial_models["Wavelet"] = copy(wavelet_distance.wt1)
del wavelet_distance
if any("MVC" in s for s in statistics):
mvc_distance = \
MVC_Distance(dataset1, dataset2,
fiducial_model=fiducial_models["MVC"],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
mvc_distance.distance_metric()
distances["MVC"] = mvc_distance.distance
if not multicore:
fiducial_models["MVC"] = copy(mvc_distance.mvc1)
del mvc_distance
if any("PSpec" in s for s in statistics):
pspec_distance = \
PSpec_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['PSpec'],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
pspec_distance.distance_metric()
distances["PSpec"] = pspec_distance.distance
if not multicore:
fiducial_models["PSpec"] = copy(pspec_distance.pspec1)
del pspec_distance
if any("Bispectrum" in s for s in statistics):
bispec_distance = \
BiSpectrum_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Bispectrum'])
bispec_distance.distance_metric()
distances["Bispectrum"] = bispec_distance.distance
if not multicore:
fiducial_models["Bispectrum"] = copy(bispec_distance.bispec1)
del bispec_distance
if any("DeltaVariance_Slope" in s for s in statistics) or \
any("DeltaVariance_Curve" in s for s in statistics):
# Check for how boundaries should be handled.
boundary1 = 'wrap' if periodic_bounds[0] else 'fill'
boundary2 = 'wrap' if periodic_bounds[1] else 'fill'
delvar_distance = \
DeltaVariance_Distance(dataset1["moment0"],
dataset2["moment0"],
weights1=dataset1["moment0_error"][0]**-2,
weights2=dataset2["moment0_error"][0]**-2,
fiducial_model=fiducial_models["DeltaVariance"],
xlow=spatial_range[0],
xhigh=spatial_range[1],
boundary=[boundary1, boundary2])
delvar_distance.distance_metric()
distances["DeltaVariance_Curve"] = delvar_distance.curve_distance
distances["DeltaVariance_Slope"] = delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance"] = copy(delvar_distance.delvar1)
del delvar_distance
if any("DeltaVariance_Centroid_Slope" in s for s in statistics) or \
any("DeltaVariance_Centroid_Curve" in s for s in statistics):
# Check for how boundaries should be handled.
boundary1 = 'wrap' if periodic_bounds[0] else 'fill'
boundary2 = 'wrap' if periodic_bounds[1] else 'fill'
delvar_distance = \
DeltaVariance_Distance(dataset1["centroid"],
dataset2["centroid"],
weights1=dataset1["centroid_error"][0]**-2,
weights2=dataset2["centroid_error"][0]**-2,
fiducial_model=fiducial_models["DeltaVariance_Centroid"],
xlow=spatial_range[0],
xhigh=spatial_range[1],
boundary=[boundary1, boundary2])
delvar_distance.distance_metric()
distances["DeltaVariance_Centroid_Curve"] = \
delvar_distance.curve_distance
distances["DeltaVariance_Centroid_Slope"] = \
delvar_distance.slope_distance
if not multicore:
fiducial_models["DeltaVariance_Centroid"] = \
copy(delvar_distance.delvar1)
del delvar_distance
if any("Genus" in s for s in statistics):
genus_distance = \
GenusDistance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Genus'])
genus_distance.distance_metric()
distances["Genus"] = genus_distance.distance
if not multicore:
fiducial_models["Genus"] = copy(genus_distance.genus1)
del genus_distance
if any("VCS" in s for s in statistics):
# Regrid the cube to lower spectral resolution
if any(vcs_regrid):
from spectral_cube import SpectralCube
import astropy.io.fits as fits
from analysis_funcs import spectral_regrid_cube
if vcs_regrid[0] is not None:
cube1_hdu = fits.PrimaryHDU(dataset1["cube"][0],
header=dataset1["cube"][1])
cube1 = SpectralCube.read(cube1_hdu)
cube1 = spectral_regrid_cube(cube1, int(vcs_regrid[0]))
else:
cube1 = dataset1["cube"]
if vcs_regrid[1] is not None:
cube2_hdu = fits.PrimaryHDU(dataset2["cube"][0],
header=dataset2["cube"][1])
cube2 = SpectralCube.read(cube2_hdu)
cube2 = spectral_regrid_cube(cube2, int(vcs_regrid[1]))
else:
cube2 = dataset2["cube"]
else:
cube1 = dataset1["cube"]
cube2 = dataset2["cube"]
vcs_distance = VCS_Distance(cube1,
cube2,
breaks=vcs_break,
fiducial_model=fiducial_models['VCS'])
vcs_distance.distance_metric()
distances["VCS"] = vcs_distance.distance
distances["VCS_Small_Scale"] = vcs_distance.small_scale_distance
distances["VCS_Large_Scale"] = vcs_distance.large_scale_distance
distances["VCS_Break"] = vcs_distance.break_distance
if not multicore:
fiducial_models["VCS"] = copy(vcs_distance.vcs1)
del vcs_distance
if any("VCA" in s for s in statistics):
vca_distance = VCA_Distance(dataset1["cube"],
dataset2["cube"],
breaks=vca_break,
fiducial_model=fiducial_models['VCA'],
low_cut=inertial_range[0],
high_cut=inertial_range[1])
vca_distance.distance_metric()
distances["VCA"] = vca_distance.distance
if not multicore:
fiducial_models["VCA"] = copy(vca_distance.vca1)
del vca_distance
if any("Tsallis" in s for s in statistics):
tsallis_distance = \
Tsallis_Distance(dataset1["moment0"],
dataset2["moment0"],
fiducial_model=fiducial_models['Tsallis'])
tsallis_distance.distance_metric()
distances["Tsallis"] = tsallis_distance.distance
if not multicore:
fiducial_models["Tsallis"] = copy(tsallis_distance.tsallis1)
del tsallis_distance
if any("Skewness" in s for s in statistics) or\
any("Kurtosis" in s for s in statistics):
moment_distance = \
StatMoments_Distance(dataset1["moment0"],
dataset2["moment0"], radius=5,
weights1=dataset1["moment0_error"][0]**-2,
weights2=dataset2["moment0_error"][0]**-2,
fiducial_model=fiducial_models['stat_moments'])
moment_distance.distance_metric()
distances["Skewness"] = moment_distance.skewness_distance
distances["Kurtosis"] = moment_distance.kurtosis_distance
if not multicore:
fiducial_models["stat_moments"] = \
copy(moment_distance.moments1)
del moment_distance
if any("PCA" in s for s in statistics):
pca_distance = \
PCA_Distance(dataset1["cube"],
dataset2["cube"],
fiducial_model=fiducial_models['PCA'])
pca_distance.distance_metric()
distances["PCA"] = pca_distance.distance
if not multicore:
fiducial_models["PCA"] = pca_distance.pca1
del pca_distance
if any("SCF" in s for s in statistics):
# Switch the inputs such that the save file is the "fiducial"
# or first cube input below
# if scf_saves[0] is not None:
# fid_model = SCF.load_results(scf_saves[0])
# cube1 = dataset1["cube"]
# cube2 = dataset2["cube"]
# boundary1 = "continuous" if periodic_bounds[0] else 'cut'
# boundary2 = "continuous" if periodic_bounds[1] else 'cut'
# if scf_saves[1] is not None:
# fid_model = SCF.load_results(scf_saves[1])
# cube2 = dataset1["cube"]
# cube1 = dataset2["cube"]
# boundary1 = "continuous" if periodic_bounds[1] else 'cut'
# boundary2 = "continuous" if periodic_bounds[0] else 'cut'
boundary1 = "continuous" if periodic_bounds[0] else 'cut'
boundary2 = "continuous" if periodic_bounds[1] else 'cut'
scf_distance = \
SCF_Distance(cube1, cube2,
boundary=[boundary1, boundary2],
fiducial_model=fiducial_models["SCF"])
# fiducial_model=fid_model)
scf_distance.distance_metric()
distances["SCF"] = scf_distance.distance
if not multicore:
fiducial_models["SCF"] = copy(scf_distance.scf1)
del scf_distance
if any("Cramer" in s for s in statistics):
cramer_distance = \
Cramer_Distance(dataset1["cube"],
dataset2["cube"],
noise_value1=noise_value[0],
noise_value2=noise_value[1]).distance_metric()
distances["Cramer"] = cramer_distance.distance
del cramer_distance
if any("Dendrogram_Hist" in s for s in statistics) or \
any("Dendrogram_Num" in s for s in statistics):
if dendro_saves[0] is None:
input1 = dataset1["cube"]
elif isinstance(dendro_saves[0], str):
input1 = dendro_saves[0]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
if dendro_saves[1] is None:
input2 = dataset2["cube"]
elif isinstance(dendro_saves[1], str):
input2 = dendro_saves[1]
else:
raise UserWarning("dendro_saves must be the filename of the"
" saved file.")
dendro_distance = \
DendroDistance(input1, input2,
dendro_params=dendro_params,
fiducial_model=fiducial_models['Dendrogram'],
periodic_bounds=periodic_bounds,
min_features=40)
dendro_distance.distance_metric()
distances["Dendrogram_Hist"] = dendro_distance.histogram_distance
distances["Dendrogram_Num"] = dendro_distance.num_distance
if not multicore:
fiducial_models["Dendrogram"] = copy(dendro_distance.dendro1)
del dendro_distance
if any("PDF_Hellinger" in s for s in statistics) or \
any("PDF_KS" in s for s in statistics) or \
any("PDF_Lognormal" in s for s in statistics): # or \
# any("PDF_AD" in s for s in statistics):
pdf_distance = \
PDF_Distance(dataset1["moment0"],
dataset2["moment0"],
min_val1=2 * noise_value[0],
min_val2=2 * noise_value[1])
pdf_distance.distance_metric()
distances["PDF_Hellinger"] = pdf_distance.hellinger_distance
distances["PDF_KS"] = pdf_distance.ks_distance
distances["PDF_Lognormal"] = pdf_distance.lognormal_distance
# distances["PDF_AD"] = pdf_distance.ad_distance
if not multicore:
fiducial_models["PDF"] = copy(pdf_distance.PDF1)
del pdf_distance
if multicore:
return distances
else:
return distances, fiducial_models
p.clf()
# VCA/VCS
vcs_distance = VCS_Distance(dataset1["cube"],
dataset2["cube"], breaks=-0.5)
vcs_distance.distance_metric(verbose=True, label1=label1, label2=label2)
print "VCS Distance: %s" % (vcs_distance.distance)
p.savefig(os.path.join(figure_path, "vcs_example.pdf"))
p.clf()
vca_distance = VCA_Distance(dataset1["cube"],
dataset2["cube"])
vca_distance.distance_metric(verbose=True, label1=label1, label2=label2)
print "VCA Distance: %s" % (vca_distance.distance)
p.savefig(os.path.join(figure_path, "vca_example.pdf"))
p.clf()
# Tsallis
# tsallis_distance = Tsallis_Distance(dataset1["moment0"],
# dataset2["moment0"])
# tsallis_distance.distance_metric(verbose=True)
# p.savefig(os.path.join(figure_path, "tsallis_example.pdf"))
# p.clf()